Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation
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David Windridge | Seyed Ali Ghorashi | Vahid Shah-Mansouri | Reza Shahbazian | Mohammad Nabati | Hojjat Navidan | Parisa Fard Moshiri | David Windridge | V. Shah-Mansouri | S. Ghorashi | M. Nabati | Reza Shahbazian | Hojjat Navidan | P. Moshiri
[1] Ali Imran,et al. Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks , 2019, 2019 International Conference on Computing, Networking and Communications (ICNC).
[2] Gong Zhang,et al. GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[3] Yanjiao Chen,et al. TranGAN: Generative Adversarial Network Based Transfer Learning for Social Tie Prediction , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[4] Hyunsoo Kim,et al. Learning to Discover Cross-Domain Relations with Generative Adversarial Networks , 2017, ICML.
[5] Sudharman K. Jayaweera,et al. A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.
[6] Lantao Yu,et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.
[7] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[8] Junxing Zhang,et al. GANSlicing: A GAN-Based Software Defined Mobile Network Slicing Scheme for IoT Applications , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[9] Jiankun Hu,et al. A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns , 2014, IEEE Transactions on Computers.
[10] Brian L. Evans,et al. Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement , 2017, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.
[11] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[12] Babar Shah,et al. Android malware detection through generative adversarial networks , 2019, Transactions on Emerging Telecommunications Technologies.
[13] Alexei A. Efros,et al. Toward Multimodal Image-to-Image Translation , 2017, NIPS.
[14] Jiannong Cao,et al. Middleware for Wireless Sensor Networks: A Survey , 2008, Journal of Computer Science and Technology.
[15] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Yang Li,et al. Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities , 2019, IEEE Communications Magazine.
[17] Weiwei Liu,et al. An end-to-end generative network for environmental sound-based covert communication , 2018, Multimedia Tools and Applications.
[18] Seyed Ali Ghorashi,et al. A novel smartphone application for indoor positioning of users based on machine learning , 2019, UbiComp/ISWC Adjunct.
[19] Biing-Hwang Juang,et al. Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).
[20] Yoshua Bengio,et al. Boundary-Seeking Generative Adversarial Networks , 2017, ICLR 2017.
[21] Jian Chen,et al. Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network , 2018, 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).
[22] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[23] Jia Shi,et al. Generative Adversarial Network Assisted Power Allocation for Cooperative Cognitive Covert Communication System , 2020, IEEE Communications Letters.
[24] Yiannis Demiris,et al. MAGAN: Margin Adaptation for Generative Adversarial Networks , 2017, ArXiv.
[25] Bartosz Krawczyk,et al. Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.
[26] Timothy J. O'Shea,et al. Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks , 2018, 2019 International Conference on Computing, Networking and Communications (ICNC).
[27] Chia-Liang Liu,et al. Impacts Of I/q Imbalance On Qpsk-ofdm-qam Detection , 1998, International 1998 Conference on Consumer Electronics.
[28] Andreas Mitschele-Thiel,et al. Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks , 2016, IEEE Transactions on Network and Service Management.
[29] Nan Sun,et al. WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).
[30] Kilian Q. Weinberger,et al. An empirical study on evaluation metrics of generative adversarial networks , 2018, ArXiv.
[31] Stephane Villette,et al. Speech Bandwidth Extension Using Generative Adversarial Networks , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[32] David Windridge,et al. Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach , 2020, IEEE Sensors Letters.
[33] Lorenza Giupponi,et al. From 4G to 5G: Self-organized Network Management meets Machine Learning , 2017, Comput. Commun..
[34] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[35] Dusit Niyato,et al. A Generative Adversarial Learning-Based Approach for Cell Outage Detection in Self-Organizing Cellular Networks , 2020, IEEE Wireless Communications Letters.
[36] Zhuoning Dong,et al. Aero-Engine Faults Diagnosis Based on K-Means Improved Wasserstein GAN and Relevant Vector Machine , 2019, 2019 Chinese Control Conference (CCC).
[37] Timothy J. O'Shea,et al. Physical Layer Communications System Design Over-the-Air Using Adversarial Networks , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[38] David Palacios,et al. Unsupervised Technique for Automatic Selection of Performance Indicators in Self-Organizing Networks , 2017, IEEE Communications Letters.
[39] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Yimin Zhou,et al. A Review: Generative Adversarial Networks , 2019, 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA).
[41] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[42] Roland Vollgraf,et al. Texture Synthesis with Spatial Generative Adversarial Networks , 2016, ArXiv.
[43] Changzhen Hu,et al. An Effective Method to Generate Simulated Attack Data Based on Generative Adversarial Nets , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).
[44] Kemal Davaslioglu,et al. Generative Adversarial Learning for Spectrum Sensing , 2018, 2018 IEEE International Conference on Communications (ICC).
[45] Ali Borji,et al. Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..
[46] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[47] David Windridge,et al. Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition , 2020, ArXiv.
[48] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[49] Erhan Guven,et al. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.
[50] Richard Demo Souza,et al. A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.
[51] Yalin E. Sagduyu,et al. Deep Learning for Launching and Mitigating Wireless Jamming Attacks , 2018, IEEE Transactions on Cognitive Communications and Networking.
[52] Trevor Darrell,et al. Adversarial Feature Learning , 2016, ICLR.
[53] Viktor K. Prasanna,et al. Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
[54] Purva Raut,et al. Data Augmentation using Generative models for Credit Card Fraud Detection , 2018, 2018 4th International Conference on Computing Communication and Automation (ICCCA).
[55] Paul Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[56] Aaron Smith,et al. A Communication Channel Density Estimating Generative Adversarial Network , 2019, 2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW).
[57] Stefano Ermon,et al. Generative Adversarial Imitation Learning , 2016, NIPS.
[58] Shahrokh Valaee,et al. A Survey on Behavior Recognition Using WiFi Channel State Information , 2017, IEEE Communications Magazine.
[59] A. S. Madhukumar,et al. Spectrum sensing and modulation classification for cognitive radios using cumulants based on fractional lower order statistics , 2013 .
[60] Hasan Sakir Bilge,et al. Recent Trends in Deep Generative Models: a Review , 2018, 2018 3rd International Conference on Computer Science and Engineering (UBMK).
[61] Anil K. Jain,et al. AdvFaces: Adversarial Face Synthesis , 2019, 2020 IEEE International Joint Conference on Biometrics (IJCB).
[62] Mounir Ghogho,et al. Deep learning approach for Network Intrusion Detection in Software Defined Networking , 2016, 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).
[63] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[64] Tom White,et al. Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.
[65] Ping Tan,et al. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[66] Yong-Guk Kim,et al. Android-GAN: Defending against android pattern attacks using multi-modal generative network as anomaly detector , 2020, Expert Syst. Appl..
[67] Huan Ying,et al. Power Message Generation in Smart Grid via Generative Adversarial Network , 2019, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).
[68] Mohsen Guizani,et al. Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).
[69] Yoav Goldberg,et al. Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation , 2018, ICML.
[70] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[71] Kaoru Ota,et al. Improved MalGAN: Avoiding Malware Detector by Leaning Cleanware Features , 2019, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).
[72] Sung-Bae Cho,et al. Malware Detection Using Deep Transferred Generative Adversarial Networks , 2017, ICONIP.
[73] Huy Kang Kim,et al. GIDS: GAN based Intrusion Detection System for In-Vehicle Network , 2018, 2018 16th Annual Conference on Privacy, Security and Trust (PST).
[74] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[75] Muhammad Ali Imran,et al. A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.
[76] Nabin Kumar Karn,et al. Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).
[77] Jun Zhu,et al. Triple Generative Adversarial Nets , 2017, NIPS.
[78] Yifan Wu,et al. Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition , 2018, 2018 IEEE 18th International Conference on Communication Technology (ICCT).
[79] Shahram Latifi,et al. Image Generation with Gans-based Techniques: A Survey , 2019 .
[80] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[81] Xiaoguo Wang,et al. A Fraudulent Data Simulation Method Based on Generative Adversarial Networks , 2019 .
[82] Hamed Haddadi,et al. Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.
[83] Khaled M. Elleithy,et al. A highly accurate machine learning approach for developing wireless sensor network middleware , 2018, 2018 Wireless Telecommunications Symposium (WTS).
[84] Tommaso Melodia,et al. Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey , 2019, Ad Hoc Networks.
[85] Saikat Guha,et al. Covert Wireless Communication With Artificial Noise Generation , 2017, IEEE Transactions on Wireless Communications.
[86] Matthias Schubert,et al. MMGAN: Generative Adversarial Networks for Multi-Modal Distributions , 2019, ArXiv.
[87] Qiyue Li,et al. Wavelet Transform DC-GAN for Diversity Promoted Fingerprint Construction in Indoor Localization , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).
[88] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[89] Shunming Li,et al. Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks , 2019, IEEE Access.
[90] Yuhui Zheng,et al. Recent Progress on Generative Adversarial Networks (GANs): A Survey , 2019, IEEE Access.
[91] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[92] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[93] Zhiguang Qin,et al. CsiGAN: Robust Channel State Information-Based Activity Recognition With GANs , 2019, IEEE Internet of Things Journal.
[94] Pan Wang,et al. FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN , 2019, 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom).
[95] Tathagata Mukherjee,et al. Detection of Rogue RF Transmitters using Generative Adversarial Nets , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).
[96] Laurence T. Yang,et al. A survey on deep learning for big data , 2018, Inf. Fusion.
[97] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[98] Zhi Xue,et al. IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection , 2018, PAKDD.
[99] Mohammad Ali Keyvanrad,et al. A brief survey on deep belief networks and introducing a new object oriented toolbox ( DeeBNet V 3 . 0 ) , 2016 .
[100] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[101] Kemal Davaslioglu,et al. Generative Adversarial Network for Wireless Signal Spoofing , 2019, WiseML@WiSec.
[102] Daniel L. Marino,et al. Generalization of Deep Learning for Cyber-Physical System Security: A Survey , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.
[103] Xiaojiang Du,et al. A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security , 2018, IEEE Communications Surveys & Tutorials.
[104] Siddique Latif,et al. Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).
[105] Jose Ordonez-Lucena,et al. Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges , 2017, IEEE Communications Magazine.
[106] Abhishek Kumar,et al. Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference , 2017, NIPS.
[107] Rajen B. Bhatt,et al. User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks , 2016, SocProS.
[108] Kemal Davaslioglu,et al. Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data , 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).
[109] Jon J. Aho,et al. Generating Realistic Data for Network Analytics , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).
[110] Mohammad Eshghi,et al. A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games , 2018, 2018 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC).
[111] Ala I. Al-Fuqaha,et al. Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).
[112] Chenglin Wen,et al. Deep learning fault diagnosis method based on global optimization GAN for unbalanced data , 2020, Knowl. Based Syst..
[113] Jie Li,et al. Dynamic Traffic Feature Camouflaging via Generative Adversarial Networks , 2019, 2019 IEEE Conference on Communications and Network Security (CNS).
[114] Bo Zhang,et al. Recent Advances of Generative Adversarial Networks in Computer Vision , 2019, IEEE Access.
[115] Azam Bagheri,et al. Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling , 2019, 2019 IEEE Milan PowerTech.
[116] Kun Xu,et al. A survey of image synthesis and editing with generative adversarial networks , 2017 .
[117] Dipankar Raychaudhuri,et al. Self-Organizing Cellular Radio Access Network with Deep Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
[118] Maria Rigaki,et al. Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection , 2018, 2018 IEEE Security and Privacy Workshops (SPW).
[119] Emanuele Ghelfi,et al. A Survey on GANs for Anomaly Detection , 2019, ArXiv.
[120] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[121] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[122] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[123] Fei-Yue Wang,et al. Generative adversarial networks: introduction and outlook , 2017, IEEE/CAA Journal of Automatica Sinica.
[124] Jakob Hoydis,et al. An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.
[125] Shiwen Mao,et al. CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.
[126] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[127] Daniel S. Berman,et al. A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..
[128] Muhammad Ali Imran,et al. Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.
[129] Jiann-Shiun Yuan,et al. Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).
[130] Junliang Wang,et al. AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition , 2019, IEEE Transactions on Semiconductor Manufacturing.
[131] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[132] Jun-Hai Zhai,et al. Recent Advance On Generative Adversarial Networks , 2018, 2018 International Conference on Machine Learning and Cybernetics (ICMLC).
[133] Chen Wang,et al. Wireless Sensing for Human Activity: A Survey , 2020, IEEE Communications Surveys & Tutorials.
[134] David Pfau,et al. Connecting Generative Adversarial Networks and Actor-Critic Methods , 2016, ArXiv.
[135] Hongyu Chen,et al. Generating Music Algorithm with Deep Convolutional Generative Adversarial Networks , 2019, 2019 IEEE 2nd International Conference on Electronics Technology (ICET).
[136] Ying Tan,et al. Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN , 2017, DMBD.
[137] Sergey Levine,et al. A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.
[138] Fernando Pérez-Cruz,et al. PassGAN: A Deep Learning Approach for Password Guessing , 2017, ACNS.
[139] Tao Zhang,et al. A Transfer Learning Strategy for Rotation Machinery Fault Diagnosis based on Cycle-Consistent Generative Adversarial Networks , 2018, 2018 Chinese Automation Congress (CAC).
[140] Timothy J. O'Shea,et al. Applications of Machine Learning to Cognitive Radio Networks , 2007, IEEE Wireless Communications.